The SoftCumulative Constraint with Quadratic Penalty

Authors: Yanick Ouellet, Claude-Guy Quimper3813-3820

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Section 7 shows how relevant our algorithms are in practice before concluding. We implemented our algorithms in C++ and used the lazy clause generation solver Chuffed (Chu 2011). We used the modelling language Minizinc (Nethercote et al. 2007) to implement our models. Our experiments were run on an Intel Xeon 4110 CPU with a speed of 2.10Ghz. We ran all our experiments with a timeout of 20 minutes.
Researcher Affiliation Academia Yanick Ouellet, Claude-Guy Quimper, Universit e Laval, Qu ebec, Canada yanick.ouellet.2@ulaval.ca, claude-guy.quimper@ift.ulaval.ca
Pseudocode Yes Algorithm 1: Overcost Bound(I, C, T), Algorithm 2: Soft Energetic Checker(I, C, T, Z), Algorithm 3: Soft Energetic Filtering(I, C, T, Z, f), Algorithm 4: Compute Longest Path(I, C, T, π), Algorithm 5: Adjust Cost(I, i, P)
Open Source Code No The paper does not provide an explicit statement or link for the open-source code of the methodology described.
Open Datasets Yes We used an adapted version of the KSD15 D benchmark (Kon e et al. 2011).
Dataset Splits No The paper mentions using instances from the KSD15 D benchmark but does not provide specific training/validation/test dataset splits, as it is a constraint satisfaction problem rather than a machine learning one requiring such splits.
Hardware Specification Yes Our experiments were run on an Intel Xeon 4110 CPU with a speed of 2.10Ghz.
Software Dependencies No The paper mentions using 'Chuffed (Chu 2011)' and 'Minizinc (Nethercote et al. 2007)' but does not provide specific version numbers for these software components.
Experiment Setup Yes We ran all our experiments with a timeout of 20 minutes. We adapted it by decreasing the capacity of each resource by four units and fixing the makespan to the optimal value of the original instance.